"""simple docstring"""



from __future__ import annotations

import unittest

from transformers import BlenderbotSmallConfig, BlenderbotSmallTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
   import tensorflow as tf

   from transformers import TFAutoModelForSeqaSeqLM, TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel






@require_tf
class  snake_case__      :
 _snake_case       :					Any							  =						BlenderbotSmallConfig
 _snake_case       :					Optional[Any]							  =						{}
 _snake_case       :					List[str]							  =						"""gelu"""


 def __init__( self    ,		lowerCamelCase    ,		lowerCamelCase=13    ,		lowerCamelCase=7    ,		lowerCamelCase=True    ,		lowerCamelCase=False    ,		lowerCamelCase=99    ,		lowerCamelCase=32    ,		lowerCamelCase=2    ,		lowerCamelCase=4    ,		lowerCamelCase=37    ,		lowerCamelCase=0.1    ,		lowerCamelCase=0.1    ,		lowerCamelCase=20    ,		lowerCamelCase=2    ,		lowerCamelCase=1    ,		lowerCamelCase=0    ,		):
        __a       =    parent
        __a       =    batch_size
        __a       =    seq_length
        __a       =    is_training
        __a       =    use_labels
        __a       =    vocab_size
        __a       =    hidden_size
        __a       =    num_hidden_layers
        __a       =    num_attention_heads
        __a       =    intermediate_size

        __a       =    hidden_dropout_prob
        __a       =    attention_probs_dropout_prob
        __a       =    max_position_embeddings
        __a       =    eos_token_id
        __a       =    pad_token_id
        __a       =    bos_token_id


 def 						a__   ( self						):
        __a       =    ids_tensor([self.batch_size, self.seq_length - 1]    ,		self.vocab_size						)
        __a       =    tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size						)    ,		1						)
        __a       =    tf.concat([input_ids, eos_tensor]    ,		axis=1						)

        __a       =    ids_tensor([self.batch_size, self.seq_length]    ,		self.vocab_size						)

        __a       =    self.config_cls(
            vocab_size=self.vocab_size    ,		d_model=self.hidden_size    ,		encoder_layers=self.num_hidden_layers    ,		decoder_layers=self.num_hidden_layers    ,		encoder_attention_heads=self.num_attention_heads    ,		decoder_attention_heads=self.num_attention_heads    ,		encoder_ffn_dim=self.intermediate_size    ,		decoder_ffn_dim=self.intermediate_size    ,		dropout=self.hidden_dropout_prob    ,		attention_dropout=self.attention_probs_dropout_prob    ,		max_position_embeddings=self.max_position_embeddings    ,		eos_token_ids=[2]    ,		bos_token_id=self.bos_token_id    ,		pad_token_id=self.pad_token_id    ,		decoder_start_token_id=self.pad_token_id    ,		**self.config_updates    ,		)
        __a       =    prepare_blenderbot_small_inputs_dict(lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase						)
        return config, inputs_dict






 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase						):
        __a       =    TFBlenderbotSmallModel(config=lowerCamelCase						).get_decoder()
        __a       =    inputs_dict["input_ids"]

        __a       =    input_ids[:1, :]
        __a       =    inputs_dict["attention_mask"][:1, :]
        __a       =    inputs_dict["head_mask"]
        __a       =    1

        # first forward pass
        __a       =    model(lowerCamelCase    ,		attention_mask=lowerCamelCase    ,		head_mask=lowerCamelCase    ,		use_cache=lowerCamelCase						)

        __a ,   __a       =    outputs.to_tuple()

        # create hypothetical next token and extent to next_input_ids
        __a       =    ids_tensor((self.batch_size, 3)    ,		config.vocab_size						)
        __a       =    tf.cast(ids_tensor((self.batch_size, 3)    ,		2						)    ,		tf.inta						)

        # append to next input_ids and
        __a       =    tf.concat([input_ids, next_tokens]    ,		axis=-1						)
        __a       =    tf.concat([attention_mask, next_attn_mask]    ,		axis=-1						)

        __a       =    model(lowerCamelCase    ,		attention_mask=lowerCamelCase						)[0]
        __a       =    model(lowerCamelCase    ,		attention_mask=lowerCamelCase    ,		past_key_values=lowerCamelCase						)[0]

        self.parent.assertEqual(next_tokens.shape[1]    ,		output_from_past.shape[1]						)

        # select random slice
        __a       =    int(ids_tensor((1,)    ,		output_from_past.shape[-1]						)						)
        __a       =    output_from_no_past[:, -3:, random_slice_idx]
        __a       =    output_from_past[:, :, random_slice_idx]

        # test that outputs are equal for slice
        tf.debugging.assert_near(lowerCamelCase    ,		lowerCamelCase    ,		rtol=1E-3						)



def 	_lowerCamelCase(	a   ,    a   ,    a   ,    a=None   ,    a=None   ,    a=None   ,    a=None   ,    a=None   ,    ):
       if attention_mask is None:
              __a       =    tf.cast(tf.math.not_equal(a   ,    config.pad_token_id				)   ,    tf.inta				)
       if decoder_attention_mask is None:
              __a       =    tf.concat(
                  [
                      tf.ones(decoder_input_ids[:, :1].shape   ,    dtype=tf.inta				),
                      tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]   ,    config.pad_token_id				)   ,    tf.inta				),
                  ]   ,    axis=-1   ,    )
       if head_mask is None:
              __a       =    tf.ones((config.encoder_layers, config.encoder_attention_heads)				)
       if decoder_head_mask is None:
              __a       =    tf.ones((config.decoder_layers, config.decoder_attention_heads)				)
       if cross_attn_head_mask is None:
              __a       =    tf.ones((config.decoder_layers, config.decoder_attention_heads)				)
       return {
           "input_ids": input_ids,
           "decoder_input_ids": decoder_input_ids,
           "attention_mask": attention_mask,
           "decoder_attention_mask": decoder_attention_mask,
           "head_mask": head_mask,
           "decoder_head_mask": decoder_head_mask,
           "cross_attn_head_mask": cross_attn_head_mask,
       }






@require_tf
class  snake_case__      (       snake_case_,						snake_case_,						unittest.TestCase       ):
 _snake_case       :					Union[str, Any]							  =						(
     (TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel) if is_tf_available() else ()
 )
 _snake_case       :					int							  =						(TFBlenderbotSmallForConditionalGeneration,) if is_tf_available() else ()
 _snake_case       :					Tuple							  =						(
     {
         """conversational""": TFBlenderbotSmallForConditionalGeneration,
         """feature-extraction""": TFBlenderbotSmallModel,
         """summarization""": TFBlenderbotSmallForConditionalGeneration,
         """text2text-generation""": TFBlenderbotSmallForConditionalGeneration,
         """translation""": TFBlenderbotSmallForConditionalGeneration,
     }
     if is_tf_available()
     else {}
 )
 _snake_case       :					Union[str, Any]							  =						True
 _snake_case       :					Optional[Any]							  =						False
 _snake_case       :					Optional[Any]							  =						False


 def 						a__   ( self						):
        __a       =    TFBlenderbotSmallModelTester(self						)
        __a       =    ConfigTester(self    ,		config_class=lowerCamelCase						)


 def 						a__   ( self						):
        self.config_tester.run_common_tests()






 def 						a__   ( self						):
        __a       =    self.model_tester.prepare_config_and_inputs_for_common()
        self.model_tester.check_decoder_model_past_large_inputs(*lowerCamelCase						)






@require_tokenizers
@require_tf
class  snake_case__      (       unittest.TestCase       ):
 _snake_case       :					Dict							  =						[
     """Social anxiety\nWow, I am never shy. Do you have anxiety?\nYes. I end up sweating and blushing and feel like """
     """  i'm going to throw up.\nand why is that?"""
 ]
 _snake_case       :					str							  =						"""facebook/blenderbot_small-90M"""


 @cached_property
 def 						a__   ( self						):
        # use "old" tokenizer here because of bug when downloading new tokenizer
        return BlenderbotSmallTokenizer.from_pretrained("facebook/blenderbot-90M"						)


 @cached_property
 def 						a__   ( self						):
        __a       =    TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name						)
        return model






 @slow
 def 						a__   ( self						):
        __a       =    self.tokenizer(self.src_text    ,		return_tensors="tf"						)
        __a       =    self.model.generate(
            model_inputs.input_ids    ,		attention_mask=model_inputs.attention_mask    ,		num_beams=2    ,		use_cache=lowerCamelCase    ,		)
        __a       =    self.tokenizer.batch_decode(generated_ids.numpy()    ,		skip_special_tokens=lowerCamelCase						)[0]
        assert generated_words in (
            "i don't know. i just feel like i'm going to throw up. it's not fun.",
            "i'm not sure. i just feel like i've been feeling like i have to be in a certain place",
            "i'm not sure. i just feel like i've been in a bad situation.",
        )

"""simple docstring"""



import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotSmallConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
   import os

   # The slow tests are often failing with OOM error on GPU
   # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
   # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
   SCREAMING_SNAKE_CASE__:List[Any]									=			"""platform"""

   import jax
   import jax.numpy as jnp

   from transformers.models.blenderbot_small.modeling_flax_blenderbot_small import (
       FlaxBlenderbotSmallForConditionalGeneration,
       FlaxBlenderbotSmallModel,
       shift_tokens_right,
   )



def 	_lowerCamelCase(	a   ,    a   ,    a=None   ,    a=None   ,    a=None   ,    a=None   ,    a=None   ,    a=None   ,    ):
       if attention_mask is None:
              __a       =    np.where(input_ids != config.pad_token_id   ,    1   ,    0				)
       if decoder_attention_mask is None:
              __a       =    np.where(decoder_input_ids != config.pad_token_id   ,    1   ,    0				)
       if head_mask is None:
              __a       =    np.ones((config.encoder_layers, config.encoder_attention_heads)				)
       if decoder_head_mask is None:
              __a       =    np.ones((config.decoder_layers, config.decoder_attention_heads)				)
       if cross_attn_head_mask is None:
              __a       =    np.ones((config.decoder_layers, config.decoder_attention_heads)				)
       return {
           "input_ids": input_ids,
           "decoder_input_ids": decoder_input_ids,
           "attention_mask": attention_mask,
           "decoder_attention_mask": attention_mask,
       }






class  snake_case__      :


 def __init__( self    ,		lowerCamelCase    ,		lowerCamelCase=13    ,		lowerCamelCase=7    ,		lowerCamelCase=True    ,		lowerCamelCase=False    ,		lowerCamelCase=99    ,		lowerCamelCase=16    ,		lowerCamelCase=2    ,		lowerCamelCase=4    ,		lowerCamelCase=4    ,		lowerCamelCase="gelu"    ,		lowerCamelCase=0.1    ,		lowerCamelCase=0.1    ,		lowerCamelCase=32    ,		lowerCamelCase=2    ,		lowerCamelCase=1    ,		lowerCamelCase=0    ,		lowerCamelCase=0.02    ,		):
        __a       =    parent
        __a       =    batch_size
        __a       =    seq_length
        __a       =    is_training
        __a       =    use_labels
        __a       =    vocab_size
        __a       =    hidden_size
        __a       =    num_hidden_layers
        __a       =    num_attention_heads
        __a       =    intermediate_size
        __a       =    hidden_act
        __a       =    hidden_dropout_prob
        __a       =    attention_probs_dropout_prob
        __a       =    max_position_embeddings
        __a       =    eos_token_id
        __a       =    pad_token_id
        __a       =    bos_token_id
        __a       =    initializer_range


 def 						a__   ( self						):
        __a       =    np.clip(ids_tensor([self.batch_size, self.seq_length - 1]    ,		self.vocab_size						)    ,		3    ,		self.vocab_size						)
        __a       =    np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)    ,		dtype=np.intaa						))    ,		-1						)

        __a       =    shift_tokens_right(lowerCamelCase    ,		1    ,		2						)

        __a       =    BlenderbotSmallConfig(
            vocab_size=self.vocab_size    ,		d_model=self.hidden_size    ,		encoder_layers=self.num_hidden_layers    ,		decoder_layers=self.num_hidden_layers    ,		encoder_attention_heads=self.num_attention_heads    ,		decoder_attention_heads=self.num_attention_heads    ,		encoder_ffn_dim=self.intermediate_size    ,		decoder_ffn_dim=self.intermediate_size    ,		dropout=self.hidden_dropout_prob    ,		attention_dropout=self.attention_probs_dropout_prob    ,		max_position_embeddings=self.max_position_embeddings    ,		eos_token_id=self.eos_token_id    ,		bos_token_id=self.bos_token_id    ,		pad_token_id=self.pad_token_id    ,		initializer_range=self.initializer_range    ,		use_cache=lowerCamelCase    ,		)
        __a       =    prepare_blenderbot_inputs_dict(lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase						)
        return config, inputs_dict


 def 						a__   ( self						):
        __a ,   __a       =    self.prepare_config_and_inputs()
        return config, inputs_dict


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase						):
        __a       =    20
        __a       =    model_class_name(lowerCamelCase						)

        __a       =    model.encode(inputs_dict["input_ids"]						)

        __a ,   __a       =    (
            inputs_dict["decoder_input_ids"],
            inputs_dict["decoder_attention_mask"],
        )

        __a       =    model.init_cache(decoder_input_ids.shape[0]    ,		lowerCamelCase    ,		lowerCamelCase						)
        __a       =    jnp.ones((decoder_input_ids.shape[0], max_decoder_length)    ,		dtype="i4"						)

        __a       =    jnp.broadcast_to(
            jnp.arange(decoder_input_ids.shape[-1] - 1						)[None, :]    ,		(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)    ,		)
        __a       =    model.decode(
            decoder_input_ids[:, :-1]    ,		lowerCamelCase    ,		decoder_attention_mask=lowerCamelCase    ,		past_key_values=lowerCamelCase    ,		decoder_position_ids=lowerCamelCase    ,		)

        __a       =    jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]    ,		dtype="i4"						)
        __a       =    model.decode(
            decoder_input_ids[:, -1:]    ,		lowerCamelCase    ,		decoder_attention_mask=lowerCamelCase    ,		past_key_values=outputs_cache.past_key_values    ,		decoder_position_ids=lowerCamelCase    ,		)

        __a       =    model.decode(lowerCamelCase    ,		lowerCamelCase						)

        __a       =    np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])						)						)
        self.parent.assertTrue(diff < 1E-3    ,		msg=F"Max diff is {diff}"						)






 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase						):
        __a       =    20
        __a       =    model_class_name(lowerCamelCase						)

        __a       =    model.encode(inputs_dict["input_ids"]						)

        __a ,   __a       =    (
            inputs_dict["decoder_input_ids"],
            inputs_dict["decoder_attention_mask"],
        )

        __a       =    jnp.concatenate(
            [
                decoder_attention_mask,
                jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])						),
            ]    ,		axis=-1    ,		)

        __a       =    model.init_cache(decoder_input_ids.shape[0]    ,		lowerCamelCase    ,		lowerCamelCase						)
        __a       =    jnp.broadcast_to(
            jnp.arange(decoder_input_ids.shape[-1] - 1						)[None, :]    ,		(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)    ,		)

        __a       =    model.decode(
            decoder_input_ids[:, :-1]    ,		lowerCamelCase    ,		decoder_attention_mask=lowerCamelCase    ,		past_key_values=lowerCamelCase    ,		decoder_position_ids=lowerCamelCase    ,		)
        __a       =    jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]    ,		dtype="i4"						)
        __a       =    model.decode(
            decoder_input_ids[:, -1:]    ,		lowerCamelCase    ,		past_key_values=outputs_cache.past_key_values    ,		decoder_attention_mask=lowerCamelCase    ,		decoder_position_ids=lowerCamelCase    ,		)

        __a       =    model.decode(lowerCamelCase    ,		lowerCamelCase    ,		decoder_attention_mask=lowerCamelCase						)

        __a       =    np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])						)						)
        self.parent.assertTrue(diff < 1E-3    ,		msg=F"Max diff is {diff}"						)






@require_flax
class  snake_case__      (       unittest.TestCase       ):
 _snake_case       :					Dict							  =						99


 def 						a__   ( self						):
        __a       =    np.array(
            [
                [71, 82, 18, 33, 46, 91, 2],
                [68, 34, 26, 58, 30, 82, 2],
                [5, 97, 17, 39, 94, 40, 2],
                [76, 83, 94, 25, 70, 78, 2],
                [87, 59, 41, 35, 48, 66, 2],
                [55, 13, 16, 58, 5, 2, 1],  # note padding
                [64, 27, 31, 51, 12, 75, 2],
                [52, 64, 86, 17, 83, 39, 2],
                [48, 61, 9, 24, 71, 82, 2],
                [26, 1, 60, 48, 22, 13, 2],
                [21, 5, 62, 28, 14, 76, 2],
                [45, 98, 37, 86, 59, 48, 2],
                [70, 70, 50, 9, 28, 0, 2],
            ]    ,		dtype=np.intaa    ,		)

        __a       =    input_ids.shape[0]
        __a       =    BlenderbotSmallConfig(
            vocab_size=self.vocab_size    ,		d_model=24    ,		encoder_layers=2    ,		decoder_layers=2    ,		encoder_attention_heads=2    ,		decoder_attention_heads=2    ,		encoder_ffn_dim=32    ,		decoder_ffn_dim=32    ,		max_position_embeddings=48    ,		eos_token_id=2    ,		pad_token_id=1    ,		bos_token_id=0    ,		)
        return config, input_ids, batch_size


 def 						a__   ( self						):
        __a ,   __a ,   __a       =    self._get_config_and_data()
        __a       =    FlaxBlenderbotSmallForConditionalGeneration(lowerCamelCase						)
        __a       =    lm_model(input_ids=lowerCamelCase						)
        __a       =    (batch_size, input_ids.shape[1], config.vocab_size)
        self.assertEqual(outputs["logits"].shape    ,		lowerCamelCase						)


 def 						a__   ( self						):
        __a       =    BlenderbotSmallConfig(
            vocab_size=self.vocab_size    ,		d_model=14    ,		encoder_layers=2    ,		decoder_layers=2    ,		encoder_attention_heads=2    ,		decoder_attention_heads=2    ,		encoder_ffn_dim=8    ,		decoder_ffn_dim=8    ,		max_position_embeddings=48    ,		)
        __a       =    FlaxBlenderbotSmallForConditionalGeneration(lowerCamelCase						)
        __a       =    np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]    ,		dtype=np.intaa						)
        __a       =    np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]    ,		dtype=np.intaa						)
        __a       =    lm_model(input_ids=lowerCamelCase    ,		decoder_input_ids=lowerCamelCase						)
        __a       =    (*summary.shape, config.vocab_size)
        self.assertEqual(outputs["logits"].shape    ,		lowerCamelCase						)






 def 						a__   ( self						):
        __a       =    np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]    ,		dtype=np.intaa						)
        __a       =    shift_tokens_right(lowerCamelCase    ,		1    ,		2						)
        __a       =    np.equal(lowerCamelCase    ,		1						).astype(np.floataa						).sum()
        __a       =    np.equal(lowerCamelCase    ,		1						).astype(np.floataa						).sum()
        self.assertEqual(shifted.shape    ,		input_ids.shape						)
        self.assertEqual(lowerCamelCase    ,		n_pad_before - 1						)
        self.assertTrue(np.equal(shifted[:, 0]    ,		2						).all()						)






@require_flax
class  snake_case__      (       snake_case_,						unittest.TestCase,						snake_case_       ):
 _snake_case       :					Any							  =						True
 _snake_case       :					int							  =						(
     (
         FlaxBlenderbotSmallModel,
         FlaxBlenderbotSmallForConditionalGeneration,
     )
     if is_flax_available()
     else ()
 )
 _snake_case       :					str							  =						(FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()


 def 						a__   ( self						):
        __a       =    FlaxBlenderbotSmallModelTester(self						)


 def 						a__   ( self						):
        __a ,   __a       =    self.model_tester.prepare_config_and_inputs()
        for model_class in self.all_model_classes:
               self.model_tester.check_use_cache_forward(lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase						)


 def 						a__   ( self						):
        __a ,   __a       =    self.model_tester.prepare_config_and_inputs()
        for model_class in self.all_model_classes:
               self.model_tester.check_use_cache_forward_with_attn_mask(lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase						)


 def 						a__   ( self						):
        __a ,   __a       =    self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
               with self.subTest(model_class.__name__						):
                      __a       =    self._prepare_for_class(lowerCamelCase    ,		lowerCamelCase						)
                      __a       =    model_class(lowerCamelCase						)

                      @jax.jit
                      def encode_jitted(lowerCamelCase    ,		lowerCamelCase=None    ,		**lowerCamelCase						):
                             return model.encode(input_ids=lowerCamelCase    ,		attention_mask=lowerCamelCase						)

                      with self.subTest("JIT Enabled"						):
                             __a       =    encode_jitted(**lowerCamelCase						).to_tuple()

                      with self.subTest("JIT Disabled"						):
                             with jax.disable_jit():
                                    __a       =    encode_jitted(**lowerCamelCase						).to_tuple()

                      self.assertEqual(len(lowerCamelCase						)    ,		len(lowerCamelCase						)						)
                      for jitted_output, output in zip(lowerCamelCase    ,		lowerCamelCase						):
                             self.assertEqual(jitted_output.shape    ,		output.shape						)


 def 						a__   ( self						):
        __a ,   __a       =    self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
               with self.subTest(model_class.__name__						):
                      __a       =    model_class(lowerCamelCase						)
                      __a       =    model.encode(inputs_dict["input_ids"]    ,		inputs_dict["attention_mask"]						)

                      __a       =    {
                          "decoder_input_ids": inputs_dict["decoder_input_ids"],
                          "decoder_attention_mask": inputs_dict["decoder_attention_mask"],
                          "encoder_outputs": encoder_outputs,
                      }

                      @jax.jit
                      def decode_jitted(lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase						):
                             return model.decode(
                                 decoder_input_ids=lowerCamelCase    ,		decoder_attention_mask=lowerCamelCase    ,		encoder_outputs=lowerCamelCase    ,		)

                      with self.subTest("JIT Enabled"						):
                             __a       =    decode_jitted(**lowerCamelCase						).to_tuple()

                      with self.subTest("JIT Disabled"						):
                             with jax.disable_jit():
                                    __a       =    decode_jitted(**lowerCamelCase						).to_tuple()

                      self.assertEqual(len(lowerCamelCase						)    ,		len(lowerCamelCase						)						)
                      for jitted_output, output in zip(lowerCamelCase    ,		lowerCamelCase						):
                             self.assertEqual(jitted_output.shape    ,		output.shape						)






 @slow
 def 						a__   ( self						):
        for model_class_name in self.all_model_classes:
               __a       =    model_class_name.from_pretrained("facebook/blenderbot_small-90M"						)
               # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
               __a       =    np.ones((1, 1)						) * model.config.eos_token_id
               __a       =    model(lowerCamelCase						)
               self.assertIsNotNone(lowerCamelCase						)
